Curriculum Overview

The Computer Science Engineering program at Pannadhay University Sikkim is meticulously designed to provide a comprehensive education that combines theoretical knowledge with practical application. The curriculum spans four years and includes core courses, departmental electives, science electives, and laboratory sessions aimed at developing both technical proficiency and critical thinking skills.

Year 1: Foundation Year

The first year lays the foundation for advanced studies in computer science by introducing students to essential mathematical concepts, basic programming principles, and fundamental engineering concepts. The courses are carefully selected to ensure a smooth transition into higher-level subjects while fostering curiosity and analytical thinking.

First Semester

Second Semester

Year 2: Core Engineering Principles

The second year builds upon the foundational knowledge gained in the first year by introducing core engineering principles and advanced programming concepts. Students are exposed to database systems, operating systems, computer networks, and software engineering methodologies.

Third Semester

Fourth Semester

Year 3: Specialization and Application

The third year focuses on specialization tracks where students choose elective courses based on their interests and career aspirations. This phase includes advanced topics in specialized areas such as artificial intelligence, cybersecurity, data science, software engineering, and human-computer interaction.

Fifth Semester

Sixth Semester

Year 4: Capstone and Future Preparation

The final year culminates in a comprehensive capstone project that integrates all the knowledge and skills acquired throughout the program. Students work closely with faculty mentors to design, develop, and present innovative solutions to real-world challenges.

Seventh Semester

Eighth Semester

Detailed Course Descriptions

Machine Learning: This course introduces students to the fundamental concepts and algorithms used in machine learning. It covers supervised and unsupervised learning techniques, including regression, classification, clustering, and dimensionality reduction. Students will learn how to implement these algorithms using popular libraries like Scikit-learn and TensorFlow. The course emphasizes practical implementation and real-world applications.

Cybersecurity Fundamentals: This course provides an introduction to cybersecurity principles and practices. Topics include network security, cryptography, access control, and risk management. Students will explore various threats and vulnerabilities in computer systems and learn how to protect against them using both technical and administrative controls.

Data Mining and Analytics: This course focuses on extracting valuable insights from large datasets. It covers data preprocessing, exploratory data analysis, statistical modeling, and visualization techniques. Students will learn how to use tools like Python, R, and SQL for data mining tasks and gain hands-on experience with real-world datasets.

Advanced Software Engineering: This course delves into advanced software engineering concepts such as software architecture, design patterns, testing strategies, and quality assurance. Students will learn how to apply these principles in large-scale projects and understand the role of software engineering in modern development environments.

User Experience Design: This course explores the principles and practices of user experience design. It covers human-computer interaction, usability testing, prototyping, and design thinking methodologies. Students will learn how to create intuitive and engaging interfaces for various platforms and devices.

Embedded Systems Programming: This course introduces students to embedded systems development using microcontrollers and real-time operating systems. Topics include hardware-software integration, low-level programming, interrupt handling, and system optimization. Students will gain practical experience through lab sessions involving actual embedded hardware.

Blockchain Technologies: This course provides a comprehensive overview of blockchain technology and its applications. It covers consensus mechanisms, smart contracts, decentralized applications (DApps), and cryptocurrency systems. Students will learn how to develop blockchain-based solutions using platforms like Ethereum and Hyperledger Fabric.

Deep Learning: This advanced course explores deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. It covers training techniques, optimization methods, and applications in computer vision, natural language processing, and speech recognition. Students will implement complex models using frameworks like PyTorch and TensorFlow.

Network Security: This course focuses on securing computer networks against various threats and attacks. It covers network protocols, firewalls, intrusion detection systems, and secure communication channels. Students will learn how to design and implement secure network infrastructures and respond to security incidents effectively.

Natural Language Processing: This course introduces students to techniques for processing and understanding human language using computational methods. It covers text preprocessing, sentiment analysis, named entity recognition, and machine translation. Students will learn how to build NLP systems using libraries like NLTK and spaCy.

DevOps and Cloud Computing: This course explores the principles and practices of DevOps and cloud computing. It covers continuous integration/continuous deployment (CI/CD), containerization, orchestration tools like Kubernetes, and cloud platforms such as AWS and Azure. Students will gain hands-on experience with cloud services and DevOps toolchains.

Computer Vision: This course delves into the field of computer vision and image processing. It covers image enhancement, feature extraction, object detection, and recognition techniques. Students will learn how to implement vision systems using libraries like OpenCV and TensorFlow.

IoT and Smart Devices: This course introduces students to the Internet of Things (IoT) and smart device development. Topics include sensor networks, wireless communication protocols, edge computing, and data analytics for IoT applications. Students will gain practical experience through hands-on projects involving actual IoT hardware.

Advanced Blockchain Applications: This advanced course explores cutting-edge blockchain applications beyond cryptocurrencies. It covers decentralized finance (DeFi), supply chain tracking, digital identity systems, and smart contracts in various domains. Students will learn how to develop and deploy blockchain solutions for real-world problems.

Project-Based Learning Philosophy

The Computer Science program at Pannadhay University Sikkim places a strong emphasis on project-based learning as a means of integrating theoretical knowledge with practical application. This approach ensures that students not only understand the concepts but also know how to apply them in real-world scenarios.

Mini-Projects

Mini-projects are introduced in the third and fourth semesters to provide students with early exposure to collaborative development environments. These projects typically span 2-3 months and involve small teams of 3-5 students working on a specific problem or application.

Mini-project topics are selected based on current industry trends and research areas. Students are encouraged to choose projects that align with their interests and career goals while ensuring they meet academic standards and learning objectives.

Final-Year Thesis/Capstone Project

The final-year thesis/capstone project is a comprehensive endeavor that spans the entire eighth semester. It involves extensive research, design, implementation, testing, and documentation of a significant software solution or innovation.

Students are expected to work closely with faculty mentors throughout this process. The project must demonstrate originality, technical depth, and practical relevance. A formal presentation is required at the end of the semester, where students defend their work before a panel of experts.

Evaluation Criteria

Projects are evaluated based on several criteria including:

• Technical Depth and Innovation
• Problem-Solving Approach
• Implementation Quality
• Documentation and Presentation Skills
• Team Collaboration and Leadership
• Impact and Relevance

Each project is assessed by a combination of faculty members, industry professionals, and peer reviewers to ensure fairness and comprehensiveness in evaluation.

Project Selection Process

Students select their projects through a structured process that involves proposal submission, mentor assignment, and milestone tracking. The selection process ensures that students are matched with mentors whose expertise aligns with their project interests.

Regular progress meetings and feedback sessions are conducted to monitor project development and address any challenges faced by teams. This support system helps students overcome obstacles and stay on track towards successful completion of their projects.